5  Appendix B

5.1 Setup

5.1.1 Install Packages

We install the following packages using the groundhog package manager to increase computational reproducibility.

options(repos = c(CRAN = "https://cran.r-project.org")) 

if (!requireNamespace("groundhog", quietly = TRUE)) {
    install.packages("groundhog")
}

pkgs <- c("magrittr", "data.table", "stringr", "Rmisc", "ggplot2",
          "lmtest", "sandwich", "stargazer")

groundhog::groundhog.library(pkg = pkgs,
                             date = "2024-08-01")

rm(pkgs)

5.1.2 Read Data

data      <- readRDS(file="../data/processed/full.Rda")
timeSpent <- data.table::fread(file = "../data/raw/PageTimes-2021-09-15.csv")
raw       <- data.table::fread(file="../data/raw/all_apps_wide_2021-09-15.csv")

5.2 Table B.1

::: {#tbl-B-1 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E1 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E1 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E1 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E1 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E1",
            dep.var.caption = "Dependent variable: E1",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)
Linear regressions: Treatment effects on E1
Dependent variable: E1
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction -0.962 -1.492 0.951 -2.435
(1.080) (1.760) (1.993) (1.855)
both 2.430 1.487
(1.759) (1.856)
interval 1.331 3.773**
(1.836) (1.923)
stage 2 -1.036 0.955 7.468*** 0.955 -1.634 -2.504*
(0.846) (1.500) (1.791) (1.500) (1.498) (1.391)
contradiction x stage 2 7.563*** 6.513*** 4.269* 12.098***
(1.315) (2.336) (2.232) (2.251)
interval x stage 2 -3.459* 2.126
(2.046) (2.517)
both x stage 2 -2.589 -4.833**
(2.120) (2.437)
Constant 47.893*** 46.653*** 45.161*** 46.653*** 47.984*** 49.083***
(0.747) (1.193) (1.294) (1.193) (1.395) (1.293)
Observations 3,010 1,490 1,520 1,014 1,002 994
R2 0.014 0.002 0.024 0.015 0.006 0.028
Adjusted R2 0.013 -0.002 0.021 0.012 0.003 0.025
Residual Std. Error 23.210 22.727 23.652 22.494 23.624 23.473
F Statistic 13.967*** 0.530 7.531*** 5.226*** 2.152* 9.393***
Note: p<0.1; p<0.05; p<0.01

:::

5.3 Table B.2

::: {#tbl-B-2 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E2 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E2 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E2 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E2 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E2",
            dep.var.caption = "Dependent variable: E2",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)
Linear regressions: Treatment effects on E2
Dependent variable: E2
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction -1.235 -1.497 -1.578 -0.612
(1.072) (1.779) (1.943) (1.852)
both 1.558 2.443
(1.806) (1.825)
interval 0.174 0.092
(1.867) (1.858)
stage 2 3.535*** 4.227*** -3.103** 4.227*** 2.998** 3.348**
(0.782) (1.159) (1.407) (1.159) (1.468) (1.433)
contradiction x stage 2 -5.636*** -7.331*** -3.812* -5.768***
(1.125) (1.824) (1.977) (2.059)
interval x stage 2 -0.879 0.683
(1.843) (2.041)
both x stage 2 -1.230 2.289
(1.871) (1.932)
Constant 50.681*** 50.108*** 48.611*** 50.108*** 50.282*** 51.666***
(0.757) (1.250) (1.265) (1.250) (1.387) (1.304)
Observations 3,010 1,490 1,520 1,014 1,002 994
R2 0.013 0.007 0.005 0.023 0.008 0.010
Adjusted R2 0.012 0.004 0.002 0.020 0.005 0.007
Residual Std. Error 21.997 22.066 21.941 20.890 22.813 22.276
F Statistic 12.879*** 2.214* 1.655 7.827*** 2.700** 3.462**
Note: p<0.1; p<0.05; p<0.01

:::

5.4 Table B.3

::: {#tbl-B-3 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E3 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E3 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E3 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E3 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E3",
            dep.var.caption = "Dependent variable: E3",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)
Linear regressions: Treatment effects on E3
Dependent variable: E3
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction -0.317 1.231 0.356 -2.623
(1.049) (1.814) (1.864) (1.767)
both 4.813*** 0.958
(1.752) (1.828)
interval 2.199 1.323
(1.787) (1.889)
stage 2 0.148 2.161 -5.425*** 2.161 -0.006 -1.779
(0.818) (1.477) (1.635) (1.477) (1.283) (1.469)
contradiction x stage 2 -3.721*** -7.585*** -2.558 -0.971
(1.217) (2.204) (1.972) (2.136)
interval x stage 2 -3.940* 2.675
(2.083) (2.253)
both x stage 2 -2.167 2.861
(1.956) (2.217)
Constant 48.591*** 46.278*** 47.510*** 46.278*** 48.477*** 51.091***
(0.733) (1.204) (1.357) (1.204) (1.321) (1.274)
Observations 3,010 1,490 1,520 1,014 1,002 994
R2 0.006 0.004 0.009 0.012 0.002 0.007
Adjusted R2 0.005 0.001 0.006 0.009 -0.001 0.004
Residual Std. Error 22.475 21.461 23.399 22.160 22.847 22.373
F Statistic 5.563*** 1.320 2.815** 4.050*** 0.678 2.489*
Note: p<0.1; p<0.05; p<0.01

:::

5.5 Table B.4

::: {#tbl-B-4 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E12 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E12 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E12 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E12 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E12",
            dep.var.caption = "Dependent variable: E12",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)
Linear regressions: Treatment effects on E12
Dependent variable: E12
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction -0.453 0.446 -1.087 -0.776
(1.031) (1.772) (1.754) (1.838)
both 1.672 0.451
(1.760) (1.850)
interval 2.072 0.539
(1.743) (1.783)
stage 2 1.817** 3.090** 4.460*** 3.090** -0.401 2.684*
(0.828) (1.365) (1.611) (1.365) (1.499) (1.440)
contradiction x stage 2 3.950*** 1.370 5.012** 5.592***
(1.198) (2.112) (2.050) (2.065)
interval x stage 2 -0.406 3.816*
(1.984) (2.187)
both x stage 2 -3.491* 0.150
(2.027) (2.134)
Constant 58.358*** 57.127*** 57.573*** 57.127*** 59.200*** 58.800***
(0.716) (1.230) (1.276) (1.230) (1.234) (1.258)
Observations 3,010 1,490 1,520 1,014 1,002 994
R2 0.010 0.004 0.019 0.008 0.007 0.021
Adjusted R2 0.009 0.001 0.015 0.005 0.004 0.018
Residual Std. Error 22.155 21.169 23.078 22.444 21.919 22.090
F Statistic 10.599*** 1.214 5.751*** 2.679** 2.266* 7.149***
Note: p<0.1; p<0.05; p<0.01

:::

5.6 Table B.5

::: {#tbl-B-5 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E13 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E13 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E13 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E13 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E13",
            dep.var.caption = "Dependent variable: E13",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)
Linear regressions: Treatment effects on E13
Dependent variable: E13
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction 0.922 -2.686 4.258** 1.173
(1.037) (1.743) (1.846) (1.792)
both 0.479 4.338**
(1.749) (1.786)
interval -1.358 5.586***
(1.811) (1.779)
stage 2 -1.549* -1.333 4.212*** -1.333 0.068 -3.362**
(0.873) (1.414) (1.579) (1.414) (1.581) (1.543)
contradiction x stage 2 3.721*** 5.546*** -0.192 5.848***
(1.221) (2.120) (2.054) (2.176)
interval x stage 2 -2.029 -1.726
(2.093) (2.201)
both x stage 2 1.401 -4.336**
(2.121) (2.052)
Constant 55.130*** 55.414*** 52.728*** 55.414*** 54.056*** 55.893***
(0.736) (1.200) (1.264) (1.200) (1.357) (1.273)
Observations 3,010 1,490 1,520 1,014 1,002 994
R2 0.006 0.002 0.010 0.005 0.009 0.013
Adjusted R2 0.005 -0.001 0.007 0.002 0.006 0.010
Residual Std. Error 21.788 22.070 21.473 21.291 21.762 22.259
F Statistic 5.977*** 0.717 3.041*** 1.812 3.054** 4.266***
Note: p<0.1; p<0.05; p<0.01

:::

5.7 Table B.6

::: {#tbl-B-6 .cell tbl-cap=’ ’}

ols_1 <- lm(formula = E23 ~ surprise + treated + surprise * treated,
              data = data)
  se_1  <- coeftest(x = ols_1, 
                    vcov = vcovCL(ols_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))
  
  ols_2 <- lm(formula = E23 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == FALSE))
  se_2  <- coeftest(x = ols_2, 
                    vcov = vcovCL(ols_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))
  
  ols_3 <- lm(formula = E23 ~ communication + treated + communication * treated, 
              data = data,
              subset = (surprise == TRUE))
  se_3  <- coeftest(x = ols_3, 
                    vcov = vcovCL(ols_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))
  
  ols_4 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "point"))
  se_4  <- coeftest(x = ols_4, 
                    vcov = vcovCL(ols_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))
  
  ols_5 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "interval"))
  se_5  <- coeftest(x = ols_5, 
                    vcov = vcovCL(ols_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))
  
  ols_6 <- lm(formula = E23 ~ surprise + treated + surprise * treated, 
              data = data,
              subset = (communication == "both"))
  se_6  <- coeftest(x = ols_6, 
                    vcov = vcovCL(ols_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
  
  
  
  se <- list(se_1[,2], se_2[,2], se_3[,2], se_4[,2], se_5[,2], se_6[,2])
  p  <- list(se_1[,4], se_2[,4], se_3[,4], se_4[,4], se_5[,4], se_6[,4])
  
  stargazer(ols_1, ols_2, ols_3, ols_4, ols_5, ols_6, 
            align = TRUE, 
            se = se, 
            p = p,   
            title = "Linear regressions: Treatment effects on E23",
            dep.var.caption = "Dependent variable: E23",
            dep.var.labels = " ",
            model.names = FALSE,
            column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
            covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "interval x stage 2", "both x stage 2", "Constant"),
            font.size = "scriptsize",
            type = "html", 
            df = FALSE)
Linear regressions: Treatment effects on E23
Dependent variable: E23
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction 1.513 0.254 1.193 3.086
(1.154) (2.075) (1.963) (1.959)
both 0.306 3.139
(2.028) (2.006)
interval 0.722 1.661
(2.047) (1.992)
stage 2 3.099*** 3.308** -7.562*** 3.308** 2.074 3.893**
(0.855) (1.389) (1.812) (1.389) (1.458) (1.597)
contradiction x stage 2 -10.200*** -10.869*** -7.545*** -12.211***
(1.326) (2.283) (2.198) (2.417)
interval x stage 2 0.585 -0.756
(2.116) (2.564)
both x stage 2 -1.234 2.091
(2.013) (2.447)
Constant 59.658*** 59.322*** 59.575*** 59.322*** 60.043*** 59.628***
(0.829) (1.452) (1.481) (1.453) (1.442) (1.416)
Observations 3,010 1,490 1,520 1,014 1,002 994
R2 0.020 0.005 0.027 0.026 0.012 0.024
Adjusted R2 0.019 0.001 0.023 0.023 0.009 0.021
Residual Std. Error 23.195 23.236 23.167 23.966 22.547 23.058
F Statistic 20.085*** 1.441 8.284*** 8.939*** 3.966*** 8.016***
Note: p<0.1; p<0.05; p<0.01

:::